The subject matter discussed in this section should not be assumed to be prior art merely as a result of its mention in this section. Similarly, a problem mentioned in this section or associated with the subject matter provided as background should not be assumed to have been previously recognized in the prior art. The subject matter in this section merely represents different approaches, which in and of themselves can also correspond to implementations of the claimed technology.
In today's world, we are dealing with huge data volumes, popularly referred to as “Big Data”. Web applications that serve and manage millions of Internet users, such as Facebook™, Instagram™, Twitter™, banking websites, or even online retail shops, such as Amazon.com™ or eBay™ are faced with the challenge of ingesting high volumes of data as fast as possible so that the end users can be provided with a real-time experience.
Another major contributor to Big Data is a concept and paradigm called “Internet of Things” (IoT). IoT is about a pervasive presence in the environment of a variety of things/objects that through wireless and wired connections are able to interact with each other and cooperate with other things/objects to create new applications/services. These applications/services are in areas likes smart cities (regions), smart car and mobility, smart home and assisted living, smart industries, public safety, energy and environmental protection, agriculture and tourism.
Stream processing is quickly becoming a crucial component of Big Data processing solutions for enterprises, with many popular open-source stream processing systems available today, including Apache Storm Trident™, Apache Spark™, Apache Samza™, Apache Flink™ Apache Flume™, and others. Low-latency and real-time processing is the hallmark of these systems. However, existing development environments that generate the application code for implementing real-time streaming applications have not fully adapted to the stringent low-latency requirements of these systems.
Therefore, an opportunity arises to eliminate existing code deployment techniques that introduce latency to stream processing systems. Low-latency operations and memory efficiencies may result.